High-throughput ab initio design of atomic interfaces using InterMatch.

Forming a hetero-interface is a materials-design strategy that can access an astronomically large phase space. However, the immense phase space necessitates a high-throughput approach for an optimal interface design. Here we introduce a high-throughput computational framework, InterMatch, for efficiently predicting charge transfer, strain, and superlattice structure of an interface by leveraging the databases of individual bulk materials. Specifically, the algorithm reads in the lattice vectors, density of states, and the stiffness tensors for each material in their isolated form from the Materials Project. From these bulk properties, InterMatch estimates the interfacial properties. We benchmark InterMatch predictions for the charge transfer against experimental measurements and supercell density-functional theory calculations. We then use InterMatch to predict promising interface candidates for doping transition metal dichalcogenide MoSe2. Finally, we explain experimental observation of factor of 10 variation in the supercell periodicity within a few microns in graphene/α-RuCl3 by exploring low energy superlattice structures as a function of twist angle using InterMatch. We anticipate our open-source InterMatch algorithm accelerating and guiding ever-growing interfacial design efforts. Moreover, the interface database resulting from the InterMatch searches presented in this paper can be readily accessed online.

Fractionalization in Fractional Correlated Insulating States at n±1/3 Filled Twisted Bilayer Graphene.

Fractionalization without time-reversal symmetry breaking is a long-sought-after goal in the study of correlated phenomena. The earlier proposal of correlated insulating states at n±1/3 filling in twisted bilayer graphene and recent experimental observations of insulating states at those fillings strongly suggest that moiré graphene systems provide a new platform to realize time-reversal symmetric fractionalized states. However, the nature of fractional excitations and the effect of quantum fluctuation on the fractional correlated insulating states are unknown. We show that excitations of the fractional correlated insulator phases in the strong coupling limit carry fractional charges and exhibit fractonic restricted mobility. Upon introduction of quantum fluctuations, the resonance of "lemniscate" structured operators drives the system into quantum lemniscate liquid (QLL) or quantum lemniscate solid (QLS). We find an emergent U(1)×U(1) 1-form symmetry unifies distinct motions of the fractionally charged excitations in the strong coupling limit and in the QLL phase, while providing a new mechanism for fractional excitations in two dimensions. We predict emergent Luttinger liquid behavior upon dilute doping in the strong coupling limit due to restricted mobility and discuss implications at a general n±1/3 filling.

Vaccine Strategy That Enhances the Protective Efficacy of Systemic Immunization by Establishing Lung-Resident Memory CD8 T Cells Against Influenza Infection.

Most influenza vaccines currently in use target the highly variable hemagglutinin protein to induce neutralizing antibodies and therefore require yearly reformulation. T cell-based universal influenza vaccines focus on eliciting broadly cross-reactive T-cell responses, especially the tissue-resident memory T cell (TRM) population in the respiratory tract, providing superior protection to circulating memory T cells. This study demonstrated that intramuscular (i.m.) administration of the adenovirus-based vaccine expressing influenza virus nucleoprotein (rAd/NP) elicited weak CD8 TRM responses in the lungs and airways, and yielded poor protection against lethal influenza virus challenge. However, a novel "prime-and-deploy" strategy that combines i.m. vaccination of rAd/NP with subsequent intranasal administration of an empty adenovector induced strong NP-specific CD8+ TRM cells and provided complete protection against influenza virus challenge. Overall, our results demonstrate that this "prime-and-deploy" vaccination strategy is potentially applicable to the development of universal influenza vaccines.

Domain-Dependent Surface Adhesion in Twisted Few-Layer Graphene: Platform for Moiré-Assisted Chemistry.

Twisted van der Waals multilayers are widely regarded as a rich platform to access novel electronic phases thanks to the multiple degrees of freedom available for controlling their electronic and chemical properties. Here, we propose that the stacking domains that form naturally due to the relative twist between successive layers act as an additional "knob" for controlling the behavior of these systems and report the emergence and engineering of stacking domain-dependent surface chemistry in twisted few-layer graphene. Using mid-infrared near-field optical microscopy and atomic force microscopy, we observe a selective adhesion of metallic nanoparticles and liquid water at the domains with rhombohedral stacking configurations of minimally twisted double bi- and trilayer graphene. Furthermore, we demonstrate that the manipulation of nanoparticles located at certain stacking domains can locally reconfigure the moiré superlattice in their vicinity at the micrometer scale. Our findings establish a new approach to controlling moiré-assisted chemistry and nanoengineering.

Induction of toxicity in human colon cells and organoids by size- and composition-dependent road dust.

Environmental pollution, including the annual resurgence of particulate matter derived from road dust, is a serious issue worldwide. Typically, the size of road dust is less than 10 µm; thus, road dust can penetrate into human organs, including the brain, through inhalation and intake by mouth. Therefore, the toxicity of road dust has been intensively studied in vitro and in vivo. However, in vitro systems, including 2D cell cultures, cannot mimic complex human organs, and there are several discrepancies between in vivo and human systems. Here, we used human colon cells and organoids to evaluate the cytotoxicity of particulate matter derived from road dust. The toxicity of road dust collected in industrialized and high traffic areas and NIST urban particulate matter reference samples were evaluated in 2D and 3D human colon cells as well as colon organoids and their characteristics were carefully examined. Data suggest that the size and elemental compositions of road dust can correlate with colon organoid toxicity, and thus, a more careful assessment of the size and elemental compositions of road dust should be conducted to predict its effect on human health.

Melting of generalized Wigner crystals in transition metal dichalcogenide heterobilayer Moiré systems.

Moiré superlattice systems such as transition metal dichalcogenide heterobilayers have garnered significant recent interest due to their promising utility as tunable solid state simulators. Recent experiments on a WSe2/WS2 heterobilayer detected incompressible charge ordered states that one can view as generalized Wigner crystals. The tunability of the transition metal dichalcogenide heterobilayer Moiré system presents an opportunity to study the rich set of possible phases upon melting these charge-ordered states. Here we use Monte Carlo simulations to study these intermediate phases in between incompressible charge-ordered states in the strong coupling limit. We find two distinct stripe solid states to be each preceded by distinct types of nematic states. In particular, we discover microscopic mechanisms that stabilize each of the nematic states, whose order parameter transforms as the two-dimensional E representation of the Moiré lattice point group. Our results provide a testable experimental prediction of where both types of nematic occur, and elucidate the microscopic mechanism driving their formation.

Harnessing interpretable and unsupervised machine learning to address big data from modern X-ray diffraction.

The information content of crystalline materials becomes astronomical when collective electronic behavior and their fluctuations are taken into account. In the past decade, improvements in source brightness and detector technology at modern X-ray facilities have allowed a dramatically increased fraction of this information to be captured. Now, the primary challenge is to understand and discover scientific principles from big datasets when a comprehensive analysis is beyond human reach. We report the development of an unsupervised machine learning approach, X-ray diffraction (XRD) temperature clustering (X-TEC), that can automatically extract charge density wave order parameters and detect intraunit cell ordering and its fluctuations from a series of high-volume X-ray diffraction measurements taken at multiple temperatures. We benchmark X-TEC with diffraction data on a quasi-skutterudite family of materials, (CaxSr[Formula: see text])3Rh4Sn13, where a quantum critical point is observed as a function of Ca concentration. We apply X-TEC to XRD data on the pyrochlore metal, Cd2Re2O7, to investigate its two much-debated structural phase transitions and uncover the Goldstone mode accompanying them. We demonstrate how unprecedented atomic-scale knowledge can be gained when human researchers connect the X-TEC results to physical principles. Specifically, we extract from the X-TEC-revealed selection rules that the Cd and Re displacements are approximately equal in amplitude but out of phase. This discovery reveals a previously unknown involvement of [Formula: see text] Re, supporting the idea of an electronic origin to the structural order. Our approach can radically transform XRD experiments by allowing in operando data analysis and enabling researchers to refine experiments by discovering interesting regions of phase space on the fly.

Quantum Phases of Transition Metal Dichalcogenide Moiré Systems.

Moiré systems provide a rich platform for studies of strong correlation physics. Recent experiments on heterobilayer transition metal dichalcogenide Moiré systems are exciting in that they manifest a relatively simple model system of an extended Hubbard model on a triangular lattice. Inspired by the prospect of the hetero-transition metal dichalcogenide Moiré system's potential as a solid-state-based quantum simulator, we explore the extended Hubbard model on the triangular lattice using the density matrix renormalization group. Specifically, we explore the two-dimensional phase space spanned by the key tuning parameters in the extended Hubbard model, namely, the kinetic energy strength and the further-range Coulomb interaction strengths. We find competition between Fermi fluid, chiral spin liquid, spin density wave, and charge order. In particular, our finding of the optimal further-range interaction for the chiral correlation presents a tantalizing possibility.

Strong interlayer interactions in bilayer and trilayer moiré superlattices.

Moiré superlattices constructed from transition metal dichalcogenides have demonstrated a series of emergent phenomena, including moiré excitons, flat bands, and correlated insulating states. All of these phenomena depend crucially on the presence of strong moiré potentials, yet the properties of these moiré potentials, and the mechanisms by which they can be generated, remain largely open questions. Here, we use angle-resolved photoemission spectroscopy with submicron spatial resolution to investigate an aligned WS2/WSe2 moiré superlattice and graphene/WS2/WSe2 trilayer heterostructure. Our experiments reveal that the hybridization between moiré bands in WS2/WSe2 exhibits an unusually large momentum dependence, with the splitting between moiré bands at the Γ point more than an order of magnitude larger than that at K point. In addition, we discover that the same WS2/WSe2 superlattice can imprint an unexpectedly large moiré potential on a third, separate layer of graphene (g/WS2/WSe2), suggesting new avenues for engineering two-dimensional moiré superlattices.

In vivo delivery of CRISPR-Cas9 using lipid nanoparticles enables antithrombin gene editing for sustainable hemophilia A and B therapy.

Hemophilia is a hereditary disease that remains incurable. Although innovative treatments such as gene therapy or bispecific antibody therapy have been introduced, substantial unmet needs still exist with respect to achieving long-lasting therapeutic effects and treatment options for inhibitor patients. Antithrombin (AT), an endogenous negative regulator of thrombin generation, is a potent genome editing target for sustainable treatment of patients with hemophilia A and B. In this study, we developed and optimized lipid nanoparticles (LNPs) to deliver Cas9 mRNA along with single guide RNA that targeted AT in the mouse liver. The LNP-mediated CRISPR-Cas9 delivery resulted in the inhibition of AT that led to improvement in thrombin generation. Bleeding-associated phenotypes were recovered in both hemophilia A and B mice. No active off-targets, liver-induced toxicity, and substantial anti-Cas9 immune responses were detected, indicating that the LNP-mediated CRISPR-Cas9 delivery was a safe and efficient approach for hemophilia therapy.

A "Prime and Deploy" Strategy for Universal Influenza Vaccine Targeting Nucleoprotein Induces Lung-Resident Memory CD8 T cells.

Lung-resident memory T cells (TRM) play an essential role in protecting against pulmonary virus infection. Parenteral administration of DNA vaccine is generally not sufficient to induce lung CD8 TRM cells. This study investigates whether intramuscularly administered DNA vaccine expressing the nucleoprotein (NP) induces lung TRM cells and protects against the influenza B virus. The results show that DNA vaccination poorly generates lung TRM cells and massive secondary effector CD8 T cells entering the lungs after challenge infection do not offer sufficient protection. Nonetheless, intranasal administration of non-replicating adenovirus vector expressing no Ag following priming DNA vaccination deploys NP-specific CD8 TRM cells in the lungs, which subsequently offers complete protection. This novel 'prime and deploy' strategy could be a promising regimen for a universal influenza vaccine targeting the conserved NP Ag.

Identification of Non-Fermi Liquid Physics in a Quantum Critical Metal via Quantum Loop Topography.

Non-Fermi liquid physics is ubiquitous in strongly correlated metals, manifesting itself in anomalous transport properties, such as a T-linear resistivity in experiments. However, its theoretical understanding in terms of microscopic models is lacking, despite decades of conceptual work and attempted numerical simulations. Here we demonstrate that a combination of sign-problem-free quantum Monte Carlo sampling and quantum loop topography, a physics-inspired machine-learning approach, can map out the emergence of non-Fermi liquid physics in the vicinity of a quantum critical point (QCP) with little prior knowledge. Using only three parameter points for training the underlying neural network, we are able to robustly identify a stable non-Fermi liquid regime tracing the fans of metallic QCPs at the onset of both spin-density wave and nematic order. In particular, we establish for the first time that a spin-density wave QCP commands a wide fan of non-Fermi liquid region that funnels into the quantum critical point. Our study thereby provides an important proof-of-principle example that new physics can be detected via unbiased machine-learning approaches.

Vitamin D Status and Factors Associated with Vitamin D Deficiency during the First Year of Life in Preterm Infants.

We aimed to investigate the changes in vitamin D levels and factors associated with vitamin D deficiency (VDD) during the first year of life in Korean preterm infants. We enrolled 333 preterm infants who were born at Kyungpook National University Children's Hospital between March 2013 and December 2019. 25-hydroxyvitamin D (25-OHD) levels and medical records were collected at birth, 6 months, and 12 months of age. The mean gestational age was 33.4 ± 2.3 weeks and mean 25-OHD levels at birth were 18.2 ± 13.5 ng/mL. The incidence of VDD was 82.8%, 30.6%, and 27.0% at birth, 6 months, and 12 months, respectively. The incidence of severe VDD (25-OHD < 10 ng/mL) was 31.5%, 1.5%, and 0%, at birth, 6 months, and 12 months, respectively. Among infants with severe VDD, the deficiency persisted in 49.6% at 6 months, and 35.3% at 12 months. The strongest predictor of VDD during follow-up was 25-OHD concentration at birth. Vitamin D supplementation at 400 IU/day did not affect vitamin D levels during the first year of life. Therefore, it is important to prevent neonatal VDD through maternal vitamin D supplementation during pregnancy. Further research is needed to determine the optimal vitamin D supplementation dose for Korean preterm infants.

Correlator convolutional neural networks as an interpretable architecture for image-like quantum matter data.

Image-like data from quantum systems promises to offer greater insight into the physics of correlated quantum matter. However, the traditional framework of condensed matter physics lacks principled approaches for analyzing such data. Machine learning models are a powerful theoretical tool for analyzing image-like data including many-body snapshots from quantum simulators. Recently, they have successfully distinguished between simulated snapshots that are indistinguishable from one and two point correlation functions. Thus far, the complexity of these models has inhibited new physical insights from such approaches. Here, we develop a set of nonlinearities for use in a neural network architecture that discovers features in the data which are directly interpretable in terms of physical observables. Applied to simulated snapshots produced by two candidate theories approximating the doped Fermi-Hubbard model, we uncover that the key distinguishing features are fourth-order spin-charge correlators. Our approach lends itself well to the construction of simple, versatile, end-to-end interpretable architectures, thus paving the way for new physical insights from machine learning studies of experimental and numerical data.

Strange Metals from Melting Correlated Insulators in Twisted Bilayer Graphene.

Even as the understanding of the mechanism behind correlated insulating states in magic-angle twisted bilayer graphene converges toward various kinds of spontaneous symmetry breaking, the metallic "normal state" above the insulating transition temperature remains mysterious, with its excessively high entropy and linear-in-temperature resistivity. In this Letter, we focus on the effects of fluctuations of the order parameters describing correlated insulating states at integer fillings of the low-energy flat bands on charge transport. Motivated by the observation of heterogeneity in the order-parameter landscape at zero magnetic field in certain samples, we conjecture the existence of frustrating extended-range interactions in an effective Ising model of the order parameters on a triangular lattice. The competition between short-distance ferromagnetic interactions and frustrating extended-range antiferromagnetic interactions leads to an emergent length scale that forms stripy mesoscale domains above the ordering transition. The gapless fluctuations of these heterogeneous configurations are found to be responsible for the linear-in-temperature resistivity as well as the enhanced low-temperature entropy. Our insights link experimentally observed linear-in-temperature resistivity and enhanced entropy to the strength of frustration or, equivalently, to the emergence of mesoscopic length scales characterizing order-parameter domains.

Modulation Doping via a Two-Dimensional Atomic Crystalline Acceptor.

Two-dimensional nanoelectronics, plasmonics, and emergent phases require clean and local charge control, calling for layered, crystalline acceptors or donors. Our Raman, photovoltage, and electrical conductance measurements combined with ab initio calculations establish the large work function and narrow bands of α-RuCl3 enable modulation doping of exfoliated single and bilayer graphene, chemical vapor deposition grown graphene and WSe2, and molecular beam epitaxy grown EuS. We further demonstrate proof of principle photovoltage devices, control via twist angle, and charge transfer through hexagonal boron nitride. Short-ranged lateral doping (≤65 nm) and high homogeneity are achieved in proximate materials with a single layer of α-RuCl3. This leads to the best-reported monolayer graphene mobilities (4900 cm2/(V s)) at these high hole densities (3 × 1013 cm-2) and yields larger charge transfer to bilayer graphene (6 × 1013 cm-2).

Utilization of road dust chemical profiles for source identification and human health impact assessment.

This study investigated the chemical profiles of fine urban road dust as a set of indicators for major air pollutants at sampling sites or as proxies for potential human health impacts. We examined the chemical compositions of fine particles (< 100 µm) or re-suspended ultrafine particles (< 2.5 µm) in the urban road dust collected from the cities with major emission sources of CO, NH3, NOx, PM2.5, SOx, and volatile organic compounds. The elemental compositions, including metal contents and volatile or semi-volatile organic compound species were determined to constitute comprehensive chemical profiles of the solid road dust samples. The water-extractable organic compounds and fluorescent species of the size-fractionated re-suspended fine particulate matter (RPM) were also incorporated in the chemical profiles. The metal content and aliphatic hydrocarbons could partly distinguish emission sources, and clearer distinctions were achieved with the inclusion of fluorescence excitation-emission matrix (EEM) results. The dose-response test results showed positive correlations between cytotoxicity and relative abundance of hydrocarbons or metal contents of urban road dust. The set of chemical profiles suggested in this study could be further utilized for site identification or human health impact assessment using urban road dust.

Linear resistivity and Sachdev-Ye-Kitaev (SYK) spin liquid behavior in a quantum critical metal with spin-1/2 fermions.

"Strange metals" with resistivity depending linearly on temperature T down to low T have been a long-standing puzzle in condensed matter physics. Here, we consider a lattice model of itinerant spin-[Formula: see text] fermions interacting via onsite Hubbard interaction and random infinite-ranged spin-spin interaction. We show that the quantum critical point associated with the melting of the spin-glass phase by charge fluctuations displays non-Fermi liquid behavior, with local spin dynamics identical to that of the Sachdev-Ye-Kitaev family of models. This extends the quantum spin liquid dynamics previously established in the large-M limit of [Formula: see text] symmetric models to models with physical [Formula: see text] spin-[Formula: see text] electrons. Remarkably, the quantum critical regime also features a Planckian linear-T resistivity associated with a T-linear scattering rate and a frequency dependence of the electronic self-energy consistent with the marginal Fermi liquid phenomenology.